We propose a software implementation of recent methodological advances for efficiently computing Maximum likelihood estimates in multilevel mixed effects models in the context of generalized linear and parametric survival models. Such models are often used in the analysis of longitudinal and cluster sample data arising in Epidemiological and other studies. The recent methodological advances we propose to implement make it possible to compute consistent and asymptotically unbiased maximum estimates in a much wider variety of problems, and we also propose to compute statistics validating these estimates. The "Preliminary Results" section of this proposal shows that it easy to encounter situations with Epidemiological data in which the usual mixed effect model algorithms fail, even in problems with large sample sizes if the 'within cluster' sample sizes are small. These failures are made more troublesome by the fact that the user seldom has any warning that the computational algorithm has failed. We propose to provide such a warning. Most mixed effect model software assumes a multivariate normal random effect density. We propose to allow other densities, including user specified densities, in the random effects model. We also propose to develop software with adaptive MARS like model fitting capabilities. [unreadable] [unreadable]